class_docstr: Hand-coded policy that returns random actions.
class_docstr_tok: ["Hand-coded","policy","that","returns","random","actions","."]
docstr: No weights to save.
docstr_tok: ["No","weights","to","save","."]
code: def get_weights(self) -> ModelWeights:
      """No weights to save."""
      return {}
  
file_path: ray/python/ray/rllib/examples/policy/random_policy.py
imports: from gymnasium.spaces import Box
  import numpy
  import random
  import tree
  from typing import List, Optional, Union
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.typing import ModelWeights, TensorStructType, TensorType
label_desc: Use image node_id 6 for calling the RandomPolicy obj's underlying member method code with example usage: obj.get_weights() and returns: dict